runZinb <- T
runClus <- T
NCORES <- 10
EAP: if we are going to ultimately use these functions, it would be good to add them to the package for zinbwave.
FP: yes, I agree.
compute.zinb.loglik <- function(Y, zinb){
mu = t(getMu(zinb))
theta = getTheta(zinb)
theta_mat = matrix(rep(theta, ncol(Y), ncol = ncol(Y)))
pi = t(getPi(zinb))
log( pi * (Y == 0) + (1 - pi) * dnbinom(Y, size = theta, mu = mu) )
}
compute.deviance.residuals <- function(Y, zinb){
mu_hat = t(getMu(zinb))
pi_hat = t(getPi(zinb))
Y_hat = (1 - pi_hat) * mu_hat
ll = compute.zinb.loglik(Y, zinb)
sign = 1*(Y - Y_hat > 0)
sign[sign == 0] = -1
sign * sqrt(-2 * ll)
}
This document is a temptative of workflow with the following steps
Along the worflow, use deviance residuals as adjusted values.
EAP: small request. Can everyone put a line between the beginning of a r chunk and text? It makes it nicely formated for my text editor.
Along the workflow, we want to use a unique SummarizedExperiement object carrying all the data we need.
EAP: I have updated the code to pull from a dataset on the repos that is created with the createData.R file. For now, I am filtering to the top 1000 most variable genes there, though we might want to add that to the code for the article. This will be slightly different data from Russell’s, which didn’t use all of the samples. We can adjust that decision later, or just compare the samples that are the same. (Russell’s clusterLabels are in the meta data)
EAP: zinbFit doesn’t accept data.frame objects, so currently have to have a data.matrix command. Should it be changed so that it does?
#counts<-read.table("../data/oeCufflinkCountData.txt",sep="\t",header=TRUE)
core <- read.table("../data/oeCufflinkCountData_1000Var.txt",
sep = "\t", header = TRUE)
core <- data.matrix(core)
metadata <- read.table("../data/oeMetadata.txt", sep = "\t", header = TRUE)
# symbol for samples missing from original clustering
metadata$clusterLabels[is.na(metadata$clusterLabels)] <- -2
Here we only look at the 1000 most variable genes. EAP: see note above, I’ve commented out the filtering and added it to the createData.R for now.
batch <- metadata$Batch
Cells have been processed in 18 different batches
col_batch = rep(brewer.pal(9, "Set1"), 2)
names(col_batch) = unique(batch)
table(batch)
## batch
## GBC08A GBC08B GBC09A GBC09B P01 P02 P03A P03B P04 P05
## 41 47 43 38 34 49 73 52 24 23
## P06 P10 P11 P12 P13 P14 Y01 Y04
## 53 51 54 51 60 49 65 42
We have qc measures from the data
qc <- metadata[, !names(metadata) %in% c("Batch", "Experiment", "clusterLabels")]
head(qc, 2)
## NREADS NALIGNED RALIGN TOTAL_DUP PRIMER
## OEP01_N706_S501 3313260 3167600 95.6035 47.9943 0.0154566
## OEP01_N701_S501 2902430 2757790 95.0167 45.0150 0.0182066
## PCT_RIBOSOMAL_BASES PCT_CODING_BASES PCT_UTR_BASES
## OEP01_N706_S501 2e-06 0.200130 0.230654
## OEP01_N701_S501 0e+00 0.182461 0.201810
## PCT_INTRONIC_BASES PCT_INTERGENIC_BASES PCT_MRNA_BASES
## OEP01_N706_S501 0.404205 0.165009 0.430784
## OEP01_N701_S501 0.465702 0.150027 0.384271
## MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS
## OEP01_N706_S501 0.843857 0.061028 0.521079
## OEP01_N701_S501 0.914370 0.033350 0.373993
## CreER ERCC_reads
## OEP01_N706_S501 1 10516
## OEP01_N701_S501 3022 9331
clus.labels <- metadata[, "clusterLabels"]
In published work (FP: add ref), cells have been clustered into 14 different clusters
col_clus <- c("transparent", brewer.pal(12, "Set3"), brewer.pal(8, "Set2"))
col_clus <- col_clus[1:length(unique(clus.labels))]
names(col_clus) <- sort(unique(clus.labels))
table(clus.labels)
## clus.labels
## -2 1 2 3 4 5 7 8 9 10 11 12 14 15
## 233 91 25 56 40 96 60 28 79 26 22 35 26 32
Batches are kind of confounded with the biology
table(data.frame(batch = as.vector(batch),
cluster = clus.labels))
## cluster
## batch -2 1 2 3 4 5 7 8 9 10 11 12 14 15
## GBC08A 5 0 2 12 9 0 0 0 0 0 2 0 2 9
## GBC08B 14 0 7 5 3 0 0 0 1 2 4 0 5 6
## GBC09A 13 0 1 5 9 0 0 0 1 1 0 0 6 7
## GBC09B 21 0 2 2 7 0 0 0 3 0 0 0 3 0
## P01 9 0 2 4 3 15 1 0 0 0 0 0 0 0
## P02 6 2 0 9 3 15 3 3 2 3 0 2 1 0
## P03A 36 3 0 3 0 12 2 9 4 2 0 2 0 0
## P03B 19 1 2 1 1 11 1 2 10 1 1 2 0 0
## P04 10 0 0 0 0 11 1 0 1 1 0 0 0 0
## P05 3 0 0 0 1 11 3 0 1 0 2 2 0 0
## P06 14 1 2 3 0 8 2 4 8 4 1 2 2 2
## P10 15 3 1 4 0 4 5 9 2 0 2 5 0 1
## P11 10 2 1 1 0 1 5 1 22 3 1 6 0 1
## P12 11 0 2 0 0 4 10 0 8 2 3 6 4 1
## P13 13 1 2 4 0 4 15 0 4 5 6 1 3 2
## P14 10 0 0 1 2 0 12 0 12 2 0 7 0 3
## Y01 14 47 1 1 2 0 0 0 0 0 0 0 0 0
## Y04 10 31 0 1 0 0 0 0 0 0 0 0 0 0
We have 849 cells.
dim(core)
## [1] 1000 849
core[1:3, 1:3]
## OEP01_N706_S501 OEP01_N701_S501 OEP01_N707_S507
## Cbr2 5799 3638 1448
## Cyp2f2 2158 2027 1078
## Gstm1 8763 7221 3581
Let’s create a SummarizedExperiment object to store the raw counts and information about the data, that is batches, published labels, and quality control measures.
se <- SummarizedExperiment(assays = list(rawCounts = core),
colData = metadata)
To cluster and get lineages we want to reduce the dimension of the data. We are going to use zinbwave to do so. First, let’s fit zinbwave with first K = 0 to compute normalized values (i.e. deviance residuals) adjusted for batches. We could also adjust for gene length or GC content here. We then fit zinbwave to get the dimensionality reduced matrix W specifying the number of dimension K = 50. Eventually, we will call zinbwave just once where we would have an argument in zinbFit like “compute_normalized_values” in c(TRUE, FALSE). For K = 0 and K = 50, we correct for batch effect including batches in X.
fn0 <- '../data/zinb_k0_batch.rda'
if (runZinb & !file.exists(fn0)){
mod <- model.matrix( ~ batch)
print(system.time(zinb0 <- zinbFit(core, ncores = NCORES,
K = 0, X = mod)))
save(zinb0, file = fn0)
}else{
load(fn0)
}
fn50 <- '../data/zinb_k50_batch.rda'
if (runZinb & !file.exists(fn50)){
mod <- model.matrix( ~ batch)
print(system.time(zinb50 <- zinbFit(core, ncores = NCORES,
K = 50, X = mod)))
save(zinb50, file = fn50)
}else{
load(fn50)
}
We use deviance residuals as normalized values for visualization. FP: explain rational: K=0 so residuals capture the bio adjusting for batch. Let’s check that deviance residuals look ok.
res <- compute.deviance.residuals(core, zinb0)
res[1:3,1:3]
## OEP01_N706_S501 OEP01_N701_S501 OEP01_N707_S507
## Cbr2 4.541942 -4.431585 -4.239279
## Cyp2f2 4.316532 4.301158 -4.146744
## Gstm1 4.629884 4.583829 -4.429794
Boxplot of the normalized values for each cell. It seems that correction for batches is ok.
res_order <- res[, order(as.numeric(batch))]
col_order <- as.numeric(batch)[order(as.numeric(batch))]
boxplot(res_order, main='Boxplot of normalized values\ncolor=batch',
col = col_order, staplewex = 0, outline = 0, border = col_order, xaxt = 'n')
PCA on the normalized values where color are for batches on the left and previously found clusters on the right. We want no clustering on the left side and clustering on the right side.
pca <- prcomp(t(res))
par(mfrow = c(1,2))
plot(pca$x, col = col_batch[batch], pch = 20,
main="PCA of normalized values\ncolor=batch")
plot(pca$x, col = col_clus[as.character(clus.labels)], pch = 20,
main = "PCA of normalized values\ncolor=cluster")
par(mfrow = c(1,1))
Let’s add the normalized values as an assay dataset in our SummarizedExperiment object.
assays(se)[['normalizedValues']] <- res
Let’s check that performing MDS on W we have something coherent with published clusters.
W <- getW(zinb50)
d <- dist(W)
fit <- cmdscale(d, eig = TRUE, k = 2)
plot(fit$points, col = col_clus[as.character(clus.labels)], main = 'MDS', pch = 20,
xlab = 'Component 1', ylab = 'Component 2')
legend(x = 'bottomright', legend = unique(names(col_clus)), cex = .5,
fill = unique(col_clus), title = 'Sample')
Let’s add W to the colData of our SummarizedExperiment object.
W <- data.frame(W)
colnames(W) <- paste0('W', 1:ncol(W))
colData(se) <- cbind(colData(se), W)
FP: what do you think of implemented a “compute_normalized_values” argument to zinbFit for summarizedExperiment objects where zinbFit would take as input a SummarizedExperiment object and return a summarizedExperiment object with slots added for normalized values and W?
We use clusterExperiment with W.
EAP: Fanny, we should really be using the RSEC function for all the steps of the clustering. I’ve put in the code, but I set eval=FALSE because I don’t have the data to test it. I also am not sure that we want to set the options for subsampleArgs and seqArgs since those are suppose to be sort of esoteric concerns…
FP: What about the following call to RSEC?
W <- colData(se)[, grepl('^W', colnames(colData(se)))]
W <- as.matrix(W)
fn <- '../data/RSEC_W.rda'
if (runClus & !file.exists(fn)){
print(system.time(ceObj <- RSEC(t(W), k0s = 4:15,
alphas = c(0.3), betas = c(0.9),
clusterFunction = "hierarchical01",
minSizes = 5,
ncores = NCORES,
combineProportion = 0.7,
mergeMethod = "locfdr",
mergeCutoff = 0.01)))
save(ceObj, file = fn)
}else{
load(fn)
}
## Note: Merging will be done on ' combineMany ', with clustering index 1
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## user system elapsed
## 4823.683 7.293 1038.270
plotClusters(ceObj, colPalette = c(bigPalette, rainbow(100)))
plotCoClustering(ceObj)
## Warning in .makeColors(clusters, colors = bigPalette): too many clusters to
## have unique color assignments
table(primaryClusterNamed(ceObj))
##
## -1 m1 m10 m11 m12 m13 m14 m15 m16 m2 m3 m4 m5 m6 m7 m8 m9
## 520 56 10 58 12 5 6 7 5 13 9 5 70 39 16 13 5
sum(primaryCluster(ceObj) == -1)
## [1] 520
FP: Elizabeth, we are working with the W here, does the locfdr make sense in this context?
#re-does merging simpling to make plot
mergeClusters(ceObj, mergeMethod = "locfdr",
plotInfo = "mergeMethod", cutoff = 0.05)
## Note: Merging will be done on ' combineMany ', with clustering index 2
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
## Warning in locfdr::locfdr(tstats, plot = 0): CM estimation failed, middle
## of histogram non-normal
plotHeatmap(ceObj, clusterSamplesData = "dendrogramValue",
breaks = .99)
plot(fit$points, col = col_clus[as.character(clus.labels)],
main = 'MDS W, color = published clusters', pch = 20,
xlab = 'Component1', ylab = 'Component2')
palDF <- ceObj@clusterLegend[[1]]
pal <- palDF[, 'color']
names(pal) <- palDF[, 'name']
plot(fit$points, col = pal[primaryClusterNamed(ceObj)],
main = 'MDS W, color = our clusters', pch = 20,
xlab = 'Component1', ylab = 'Component2')
table(data.frame(published = clus.labels,
ours = primaryCluster(ceObj)))
## ours
## published -1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## -2 83 24 1 16 51 2 22 3 1 0 2 12 5 3 0 0 3 2 0 1 2
## 1 25 54 1 3 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 2 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 3 0 0 0
## 3 10 0 0 4 0 0 0 0 37 1 0 0 0 0 0 3 0 0 0 0 1
## 4 9 0 0 0 28 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 5 28 18 3 2 38 1 0 2 0 0 2 2 0 0 0 0 0 0 0 0 0
## 7 10 0 0 0 47 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 8 14 12 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 40 2 0 0 3 0 1 0 0 0 0 2 28 0 3 0 0 0 0 0 0
## 10 19 0 0 0 0 0 0 0 0 0 0 0 4 0 2 0 1 0 0 0 0
## 11 9 0 0 0 0 0 0 0 6 0 0 0 0 2 0 3 0 0 0 0 2
## 12 4 0 0 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 0 0
## 14 16 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 2 0 4 0
## 15 18 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0
So, let’s look at a heatmap on normalized values.
sampleData <- data.frame(ours = primaryCluster(ceObj))
plotHeatmap(assays(se)$normalizedValues,
main = 'Normalized values, 1000 most variable genes',
clusterSamplesData = ceObj@dendro_samples,
sampleData = as.matrix(sampleData))
## Warning in .Method(..., deparse.level = deparse.level): number of rows of
## result is not a multiple of vector length (arg 1)
The goal of this section is to see if we need to refit zinbwave when we want to run slingshot. We run slingshot in the supervised and unsupervised mode and we try k=2, k=3, k=4 in W.
FP: We need to work on the cluster coloring (!) and the matching with the clusters you found for the paper with Russell, maybe by changing the parameters in clusterMany?
Kvec <- c(2, 3, 4)
our_cl <- as.factor(primaryClusterNamed(ceObj))
cl = our_cl[our_cl != "-1"]
for (k in Kvec){
X <- W[our_cl != "-1", 1:k]
lineages <- get_lineages(X, clus.labels = cl, start.clus = "m1")
curves <- get_curves(X, clus.labels = cl, lineages = lineages)
plot_curves(X, cl, curves, col.clus = pal)
plot_tree(X, cl, lineages, col.clus = pal)
print(paste0("K=", k))
print(lineages$lineage1)
print(lineages$lineage2)
print(lineages$lineage3)
print(lineages$lineage4)
}
## [1] "K=2"
## [1] "m1" "m5" "m16" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m16" "m11" "m13" "m17" "m19"
## [1] "m1" "m5" "m4" "m2" "m10" "m7"
## [1] "m1" "m3" "m20" "m15" "m8"
## [1] "K=3"
## [1] "m1" "m5" "m2" "m7" "m10" "m4" "m11" "m16" "m3" "m20" "m15"
## [12] "m8"
## [1] "m1" "m5" "m2" "m7" "m10" "m4" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m2" "m7" "m10" "m4" "m11" "m16" "m12" "m14"
## [1] "m1" "m5" "m2" "m7" "m10" "m4" "m11" "m13" "m17" "m19"
## [1] "K=4"
## [1] "m1" "m5" "m2" "m10" "m4" "m16" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m2" "m10" "m4" "m16" "m11" "m13" "m17" "m19"
## [1] "m1" "m5" "m2" "m10" "m4" "m16" "m11" "m6"
## [1] "m1" "m5" "m2" "m10" "m4" "m16" "m12" "m14"
for (k in Kvec){
X <- W[our_cl != "-1", 1:k]
lineages <- get_lineages(X, clus.labels = cl, start.clus = "m1",
end.clus = c("m8", "m4"))
curves <- get_curves(X, clus.labels = cl, lineages = lineages)
plot_curves(X, cl, curves, col.clus = pal)
plot_tree(X, cl, lineages, col.clus = pal)
print(paste0("K=", k))
print(lineages$lineage1)
print(lineages$lineage2)
print(lineages$lineage3)
print(lineages$lineage4)
}
## [1] "K=2"
## [1] "m1" "m5" "m16" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m16" "m11" "m13" "m17" "m19"
## [1] "m1" "m5" "m2" "m10" "m7"
## [1] "m1" "m3" "m20" "m15" "m8"
## [1] "K=3"
## [1] "m1" "m5" "m3" "m16" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m3" "m16" "m11" "m13" "m17" "m19"
## [1] "m1" "m5" "m2" "m7" "m10" "m4"
## [1] "m1" "m5" "m3" "m16" "m11" "m6"
## [1] "K=4"
## [1] "m1" "m5" "m3" "m16" "m11" "m13" "m17" "m9"
## [1] "m1" "m5" "m3" "m16" "m11" "m13" "m17" "m19"
## [1] "m1" "m5" "m3" "m16" "m11" "m6"
## [1] "m1" "m5" "m3" "m20" "m15" "m8"
mod <- model.matrix(~ batch[our_cl != "-1"])
fn <- '../data/refit_zinbwave_slingshot.rda'
if (runZinb & !file.exists(fn)){
zinbList <- lapply(Kvec, function(k){
zinbFit(core[, our_cl != "-1"], X = mod, K = k, ncores = NCORES)
})
save(zinbList, file = fn)
}else{
load(fn)
}
for(k in Kvec) {
X <- getW(zinbList[[k - 1]])[, 1:k]
lineages <- get_lineages(X, clus.labels = cl, start.clus = "m1")
curves <- get_curves(X, clus.labels = cl, lineages = lineages)
plot_curves(X, cl, curves, col.clus = pal)
plot_tree(X, cl, lineages, col.clus = pal)
print(paste0("K=", k))
print(lineages$lineage1)
print(lineages$lineage2)
print(lineages$lineage3)
print(lineages$lineage4)
}
## [1] "K=2"
## [1] "m1" "m2" "m4" "m16" "m12" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m2" "m4" "m16" "m6"
## [1] "m1" "m2" "m4" "m16" "m11"
## [1] "m1" "m2" "m4" "m16" "m13"
## [1] "K=3"
## [1] "m1" "m2" "m10" "m4" "m16" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m2" "m10" "m4" "m16" "m11" "m6"
## [1] "m1" "m2" "m10" "m4" "m16" "m14" "m12"
## [1] "m1" "m2" "m10" "m4" "m16" "m11" "m13"
## [1] "K=4"
## [1] "m1" "m2" "m11" "m16" "m12" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m2" "m11" "m16" "m4" "m10"
## [1] "m1" "m5" "m3" "m20" "m15"
## [1] "m1" "m2" "m11" "m6"
for(k in Kvec){
X <- getW(zinbList[[k - 1]])[, 1:k]
lineages <- get_lineages(X, clus.labels = cl, start.clus = "m1",
end.clus = c("m8", "m4"))
curves <- get_curves(X, clus.labels = cl, lineages = lineages)
plot_curves(X, cl, curves, col.clus = pal)
plot_tree(X, cl, lineages, col.clus = pal)
print(paste0("K=", k))
print(lineages$lineage1)
print(lineages$lineage2)
print(lineages$lineage3)
print(lineages$lineage4)
}
## [1] "K=2"
## [1] "m1" "m2" "m16" "m12" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m5" "m3" "m15" "m20"
## [1] "m1" "m2" "m10" "m4"
## [1] "m1" "m2" "m16" "m6"
## [1] "K=3"
## [1] "m1" "m2" "m11" "m16" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m2" "m11" "m16" "m14" "m12"
## [1] "m1" "m5" "m3" "m20" "m15"
## [1] "m1" "m2" "m11" "m16" "m18"
## [1] "K=4"
## [1] "m1" "m2" "m11" "m16" "m12" "m14" "m19" "m17" "m9" "m8"
## [1] "m1" "m2" "m11" "m16" "m4"
## [1] "m1" "m5" "m3" "m20" "m15"
## [1] "m1" "m2" "m11" "m6"
Here is the kind of plots we want to be able to have
de <- read.csv('../data/oe_markers.txt',
stringsAsFactors = F, header = F)
de <- de$V1
nv <- assays(se)$normalizedValues
plotHeatmap(nv[rownames(nv) %in% de, ],
main = 'Normalized values, 1000 most variable genes',
clusterSamplesData = ceObj@dendro_samples,
sampleData = as.matrix(sampleData$ours))
## Warning in .Method(..., deparse.level = deparse.level): number of rows of
## result is not a multiple of vector length (arg 1)
sessionInfo()
## R version 3.3.3 (2017-03-06)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS release 6.9 (Final)
##
## locale:
## [1] LC_CTYPE=ja_JP.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=ja_JP.UTF-8 LC_COLLATE=ja_JP.UTF-8
## [5] LC_MONETARY=ja_JP.UTF-8 LC_MESSAGES=ja_JP.UTF-8
## [7] LC_PAPER=ja_JP.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=ja_JP.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 methods stats graphics grDevices utils
## [8] datasets base
##
## other attached packages:
## [1] rARPACK_0.11-0 digest_0.6.12
## [3] RColorBrewer_1.1-2 Rtsne_0.13
## [5] matrixStats_0.52.2 magrittr_1.5
## [7] gplots_3.0.1 ggplot2_2.2.1
## [9] slingshot_0.0.3-5 princurve_1.1-12
## [11] zinbwave_0.99.1 clusterExperiment_1.3.0-9006
## [13] scone_0.99.0 scRNAseq_1.0.0
## [15] SummarizedExperiment_1.4.0 Biobase_2.34.0
## [17] GenomicRanges_1.26.4 GenomeInfoDb_1.10.3
## [19] IRanges_2.8.2 S4Vectors_0.12.2
## [21] BiocGenerics_0.20.0 knitr_1.16
##
## loaded via a namespace (and not attached):
## [1] shinydashboard_0.6.0 R.utils_2.5.0
## [3] RSQLite_1.1-2 AnnotationDbi_1.36.2
## [5] htmlwidgets_0.8 grid_3.3.3
## [7] trimcluster_0.1-2 BiocParallel_1.8.2
## [9] RNeXML_2.0.7 DESeq_1.26.0
## [11] munsell_0.4.3 codetools_0.2-15
## [13] statmod_1.4.29 scran_1.2.2
## [15] DT_0.2 miniUI_0.1.1
## [17] colorspace_1.3-2 uuid_0.1-2
## [19] pspline_1.0-17 robustbase_0.92-7
## [21] NMF_0.20.6 tximport_1.2.0
## [23] hwriter_1.3.2 rhdf5_2.18.0
## [25] rprojroot_1.2 EDASeq_2.8.0
## [27] diptest_0.75-7 R6_2.2.1
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## [31] taxize_0.8.4 locfit_1.5-9.1
## [33] flexmix_2.3-14 bitops_1.0-6
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## [45] genefilter_1.56.0 splines_3.3.3
## [47] rtracklayer_1.34.2 lazyeval_0.2.0
## [49] hexbin_1.27.1 yaml_2.1.14
## [51] reshape2_1.4.2 abind_1.4-5
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